Relevant bibliographies by topics / Spectral graph analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Spectral graph analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Spectral graph analysis"

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Cvetkovic, Dragos. "Spectral recognition of graphs." Yugoslav Journal of Operations Research 22, no. 2 (2012): 145–61. http://dx.doi.org/10.2298/yjor120925025c.

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At some time, in the childhood of spectral graph theory, it was conjectured that non-isomorphic graphs have different spectra, i.e. that graphs are characterized by their spectra. Very quickly this conjecture was refuted and numerous examples and families of non-isomorphic graphs with the same spectrum (cospectral graphs) were found. Still some graphs are characterized by their spectra and several mathematical papers are devoted to this topic. In applications to computer sciences, spectral graph theory is considered as very strong. The benefit of using graph spectra in treating graphs is that
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Arsic, Branko, Dragos Cvetkovic, Slobodan Simic, and Milan Skaric. "Graph spectral techniques in computer sciences." Applicable Analysis and Discrete Mathematics 6, no. 1 (2012): 1–30. http://dx.doi.org/10.2298/aadm111223025a.

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We give a survey of graph spectral techniques used in computer sciences. The survey consists of a description of particular topics from the theory of graph spectra independently of the areas of Computer science in which they are used. We have described the applications of some important graph eigenvalues (spectral radius, algebraic connectivity, the least eigenvalue etc.), eigenvectors (principal eigenvector, Fiedler eigenvector and other), spectral reconstruction problems, spectra of random graphs, Hoffman polynomial, integral graphs etc. However, for each described spectral technique we indi
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Guo, Haiyan, та Bo Zhou. "On the α-spectral radius of graphs". Applicable Analysis and Discrete Mathematics, № 00 (2020): 22. http://dx.doi.org/10.2298/aadm180210022g.

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For 0 ? ? ? 1, Nikiforov proposed to study the spectral properties of the family of matrices A?(G) = ?D(G)+(1 ? ?)A(G) of a graph G, where D(G) is the degree diagonal matrix and A(G) is the adjacency matrix of G. The ?-spectral radius of G is the largest eigenvalue of A?(G). For a graph with two pendant paths at a vertex or at two adjacent vertices, we prove results concerning the behavior of the ?-spectral radius under relocation of a pendant edge in a pendant path. We give upper bounds for the ?-spectral radius for unicyclic graphs G with maximum degree ? ? 2, connected irregular graphs with
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Hora, Akihito. "Central Limit Theorems and Asymptotic Spectral Analysis on Large Graphs." Infinite Dimensional Analysis, Quantum Probability and Related Topics 01, no. 02 (1998): 221–46. http://dx.doi.org/10.1142/s0219025798000144.

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Regarding the adjacency matrix of a graph as a random variable in the framework of algebraic or noncommutative probability, we discuss a central limit theorem in which the size of a graph grows in several patterns. Various limit distributions are observed for some Cayley graphs and some distance-regular graphs. To obtain the central limit theorem of this type, we make combinatorial analysis of mixed moments of noncommutative random variables on one hand, and asymptotic analysis of spectral structure of the graph on the other hand.
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Coutino, Mario, Sundeep Prabhakar Chepuri, Takanori Maehara, and Geert Leus. "Fast Spectral Approximation of Structured Graphs with Applications to Graph Filtering." Algorithms 13, no. 9 (2020): 214. http://dx.doi.org/10.3390/a13090214.

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To analyze and synthesize signals on networks or graphs, Fourier theory has been extended to irregular domains, leading to a so-called graph Fourier transform. Unfortunately, different from the traditional Fourier transform, each graph exhibits a different graph Fourier transform. Therefore to analyze the graph-frequency domain properties of a graph signal, the graph Fourier modes and graph frequencies must be computed for the graph under study. Although to find these graph frequencies and modes, a computationally expensive, or even prohibitive, eigendecomposition of the graph is required, the
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Frangioni, Antonio, and Stefano Serra Capizzano. "Spectral Analysis of (Sequences of) Graph Matrices." SIAM Journal on Matrix Analysis and Applications 23, no. 2 (2001): 339–48. http://dx.doi.org/10.1137/s089547989935366x.

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Musulin, Estanislao. "Spectral Graph Analysis for Process Monitoring." Industrial & Engineering Chemistry Research 53, no. 25 (2014): 10404–16. http://dx.doi.org/10.1021/ie403966v.

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Alon, N., I. Dinur, E. Friedgut, and B. Sudakov. "Graph Products, Fourier Analysis and Spectral Techniques." Geometric And Functional Analysis 14, no. 5 (2004): 913–40. http://dx.doi.org/10.1007/s00039-004-0478-3.

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Stanković, Ljubiša, Jonatan Lerga, Danilo Mandic, Miloš Brajović, Cédric Richard, and Miloš Daković. "From Time–Frequency to Vertex–Frequency and Back." Mathematics 9, no. 12 (2021): 1407. http://dx.doi.org/10.3390/math9121407.

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The paper presents an analysis and overview of vertex–frequency analysis, an emerging area in graph signal processing. A strong formal link of this area to classical time–frequency analysis is provided. Vertex–frequency localization-based approaches to analyzing signals on the graph emerged as a response to challenges of analysis of big data on irregular domains. Graph signals are either localized in the vertex domain before the spectral analysis is performed or are localized in the spectral domain prior to the inverse graph Fourier transform is applied. The latter approach is the spectral for
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Sun, Miao, Elvin Isufi, Natasja M. S. de Groot, and Richard C. Hendriks. "Graph-time spectral analysis for atrial fibrillation." Biomedical Signal Processing and Control 59 (May 2020): 101915. http://dx.doi.org/10.1016/j.bspc.2020.101915.

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Dissertations / Theses on the topic "Spectral graph analysis"

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Fairbanks, James Paul. "Graph analysis combining numerical, statistical, and streaming techniques." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54972.

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Graph analysis uses graph data collected on a physical, biological, or social phenomena to shed light on the underlying dynamics and behavior of the agents in that system. Many fields contribute to this topic including graph theory, algorithms, statistics, machine learning, and linear algebra. This dissertation advances a novel framework for dynamic graph analysis that combines numerical, statistical, and streaming algorithms to provide deep understanding into evolving networks. For example, one can be interested in the changing influence structure over time. These disparate techniques each co
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Valdivia, Paola Tatiana Llerena. "Graph signal processing for visual analysis and data exploration." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-15102018-165426/.

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Signal processing is used in a wide variety of applications, ranging from digital image processing to biomedicine. Recently, some tools from signal processing have been extended to the context of graphs, allowing its use on irregular domains. Among others, the Fourier Transform and the Wavelet Transform have been adapted to such context. Graph signal processing (GSP) is a new field with many potential applications on data exploration. In this dissertation we show how tools from graph signal processing can be used for visual analysis. Specifically, we proposed a data filtering method, based on
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Erdem, Ozge. "Computation And Analysis Of Spectra Of Large Undirected Networks." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612233/index.pdf.

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Many interacting complex systems in biology, in physics, in technology and social systems, can be represented in a form of large networks. These large networks are mathematically represented by graphs. A graph is represented usually by the adjacency or the Laplacian matrix. Important features of the underlying structure and dynamics of them can be extracted from the analysis of the spectrum of the graphs. Spectral analysis of the so called normalized Laplacian of large networks became popular in the recent years. The Laplacian matrices of the empirical networks are in form of unstructured larg
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Sariaydin, Ayse. "Computation And Analysis Of Spectra Of Large Networks With Directed Graphs." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612249/index.pdf.

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Analysis of large networks in biology, science, technology and social systems have become very popular recently. These networks are mathematically represented as graphs. The task is then to extract relevant qualitative information about the empirical networks from the analysis of these graphs. It was found that a graph can be conveniently represented by the spectrum of a suitable difference operator, the normalized graph Laplacian, which underlies diffusions and random walks on graphs. When applied to large networks, this requires computation of the spectrum of large matrices. The normalized
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Wang, Kaijun. "Graph-based Modern Nonparametrics For High-dimensional Data." Diss., Temple University Libraries, 2019. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/578840.

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Statistics<br>Ph.D.<br>Developing nonparametric statistical methods and inference procedures for high-dimensional large data have been a challenging frontier problem of statistics. To attack this problem, in recent years, a clear rising trend has been observed with a radically different viewpoint--``Graph-based Nonparametrics," which is the main research focus of this dissertation. The basic idea consists of two steps: (i) representation step: code the given data using graphs, (ii) analysis step: apply statistical methods on the graph-transformed problem to systematically tackle various types
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Kang, U. "Mining Tera-Scale Graphs: Theory, Engineering and Discoveries." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/160.

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How do we find patterns and anomalies, on graphs with billions of nodes and edges, which do not fit in memory? How to use parallelism for such Tera- or Peta-scale graphs? In this thesis, we propose PEGASUS, a large scale graph mining system implemented on the top of the HADOOP platform, the open source version of MAPREDUCE. PEGASUS includes algorithms which help us spot patterns and anomalous behaviors in large graphs. PEGASUS enables the structure analysis on large graphs. We unify many different structure analysis algorithms, including the analysis on connected components, PageRank, and radi
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Farina, Sofia. "A physical interpretation of network laplacian: role of perturbations and masses." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16345/.

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Il presente elaborato si propone di studiare il laplaciano associato ad un network, oggetto di interesse sia perchè dalla sua analisi spettrale è possibile ricavare delle tecniche di ricostruzione e rappresentazione della rete efficienti e al contempo semplici da implementare, ma anche per la sua possibile intepretazione fisica. Il lavoro si struttura in due sezioni: la prima, riguardante l'analisi numerica dello spettro del laplaciano di un network con particolari proprietà di simmetria e regolarità anche in seguito alla sua perturbazione in termini di rimozone casuale di nodi e di link; la
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Egidi, Michela. "Geometry, dynamics and spectral analysis on manifolds : the Pestov Identity on frame bundles and eigenvalue asymptotics on graph-like manifolds." Thesis, Durham University, 2015. http://etheses.dur.ac.uk/11306/.

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This dissertation is made up of two independent parts. In Part I we consider the Pestov Identity, an identity stated for smooth functions on the tangent bundle of a manifold and linking the Riemannian curvature tensor to the generators of the geodesic flow, and we lift it to the bundle of k-tuples of tangent vectors over a compact manifold M of dimension n. We also derive an integrated version over the bundle of orthonormal k-frames of M as well as a restriction to smooth functions on such a bundle. Finally, we present a dynamical application for the parallel transport of the Grassmannian of o
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Raak, Fredrik. "Investigation of Power Grid Islanding Based on Nonlinear Koopman Modes." Thesis, KTH, Elektriska energisystem, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-136834.

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To view the electricity supply in our society as just sockets mountedin our walls with a constant voltage output is far from the truth. Inreality, the power system supplying the electricity or the grid, is themost complex man-made dynamical system there is. It demands severecontrol and safety measures to ensure a reliable supply of electric power.Throughout the world, incidents of widespread power grid failures havebeen continuously reported. The state where electricity delivery to customersis terminated by a disturbance is called a blackout. From a stateof seemingly stable operating condition
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Kadavankandy, Arun. "L’analyse spectrale des graphes aléatoires et son application au groupement et l’échantillonnage." Thesis, Université Côte d'Azur (ComUE), 2017. http://www.theses.fr/2017AZUR4059/document.

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Dans cette thèse, nous étudions les graphes aléatoires en utilisant des outils de la théorie des matrices aléatoires et l’analyse probabilistique afin de résoudre des problèmes clefs dans le domaine des réseaux complexes et Big Data. Le premier problème qu’on considère est de détecter un sous graphe Erdős–Rényi G(m,p) plante dans un graphe Erdős–Rényi G(n,q). Nous dérivons les distributions d’une statistique basée sur les propriétés spectrales d’une matrice définie du graphe. Ensuite, nous considérons le problème de la récupération des sommets du sous graphe en présence de l’information supplé
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Books on the topic "Spectral graph analysis"

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Spectral analysis on graph-like spaces. Springer-Verlag, 2012.

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Post, Olaf. Spectral Analysis on Graph-like Spaces. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-23840-6.

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Obata, Nobuaki. Spectral Analysis of Growing Graphs. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3506-7.

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Graph spectra for complex networks. Cambridge University Press, 2011.

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1973-, Warzel Simone, ed. Random operators: Disorder effects on quantum spectra and dynamics. American Mathematical Society, 2015.

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Pierantozzi, Donald. Topics in Spectral Analysis: Algebraic Graph Theory. Independently Published, 2020.

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Spectral Clustering and Biclustering: Learning Large Graphs and Contingency Tables. Wiley, 2013.

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Bolla, Marianna. Spectral Clustering and Biclustering: Learning Large Graphs and Contingency Tables. Wiley & Sons, Incorporated, John, 2013.

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(Foreword), L. Accardi, ed. Quantum Probability and Spectral Analysis of Graphs (Theoretical and Mathematical Physics). Springer, 2007.

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Mieghem, Piet Van. Graph Spectra for Complex Networks. Cambridge University Press, 2012.

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Book chapters on the topic "Spectral graph analysis"

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Obata, Nobuaki. "Graph Products and Asymptotics." In Spectral Analysis of Growing Graphs. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3506-7_7.

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Ferrer, Miquel, Francesc Serratosa, and Alberto Sanfeliu. "Synthesis of Median Spectral Graph." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11492542_18.

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Wang, Yue, Xintao Wu, and Leting Wu. "Differential Privacy Preserving Spectral Graph Analysis." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37456-2_28.

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Qiu, Huaijun, and Edwin R. Hancock. "Commute Times for Graph Spectral Clustering." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11556121_17.

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Gao, Jing, Nan Du, Wei Fan, Deepak Turaga, Srinivasan Parthasarathy, and Jiawei Han. "A Multi-graph Spectral Framework for Mining Multi-source Anomalies." In Graph Embedding for Pattern Analysis. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4457-2_9.

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Robles-Kelly, Antonio. "Segmentation via Graph-Spectral Methods and Riemannian Geometry." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11556121_81.

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Castro-Ospina, Andrés Eduardo, Andrés Marino Álvarez-Meza, and César Germán Castellanos-Domínguez. "Automatic Graph Building Approach for Spectral Clustering." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41822-8_24.

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De Stefano, Domenico. "Spectral Graph Theory Tools for Social Network Comparison." In Classification and Multivariate Analysis for Complex Data Structures. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13312-1_14.

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Masoumi, Majid, Mahsa Rezaei, and A. Ben Hamza. "Shape Analysis of Carpal Bones Using Spectral Graph Wavelets." In Signals and Communication Technology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03574-7_12.

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Bern, Marshall, and David Goldberg. "EigenMS: De Novo Analysis of Peptide Tandem Mass Spectra by Spectral Graph Partitioning." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11415770_27.

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Conference papers on the topic "Spectral graph analysis"

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Šutić, Davor, and Ervin Varga. "Spectral Graph Analysis with Apache Spark." In the 2018 International Conference. ACM Press, 2018. http://dx.doi.org/10.1145/3274250.3275111.

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Malik, Muhammad Zubair, and Sarfraz Khurshid. "Dynamic Shape Analysis Using Spectral Graph Properties." In 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation (ICST). IEEE, 2012. http://dx.doi.org/10.1109/icst.2012.101.

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Smarandache, Roxana, and Mark F. Flanagan. "Spectral Graph Analysis of Quasi-Cyclic Codes." In GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference. IEEE, 2009. http://dx.doi.org/10.1109/glocom.2009.5425400.

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Jovanović, Nenad, Zoran Jovanović, and Aleksandar Jevremović. "Complex Networks Analysis by Spectral Graph Theory." In Sinteza 2017. Singidunum University, 2017. http://dx.doi.org/10.15308/sinteza-2017-182-185.

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Zhang, Shuhan, Fan Yang, Xuan Zeng, Dian Zhou, Shun Li, and Xiangdong Hu. "Efficient spectral graph sparsification via Krylov-subspace based spectral perturbation analysis." In 2017 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2017. http://dx.doi.org/10.1109/iscas.2017.8050468.

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Kirmani, Shad, and Kamesh Madduri. "Spectral Graph Drawing: Building Blocks and Performance Analysis." In 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2018. http://dx.doi.org/10.1109/ipdpsw.2018.00053.

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Leandro, Jorge De Jesus Gomes, Roberto Marcondes Cesar Jr, and Rogerio Schmidt Feris. "Shape Analysis Using the Spectral Graph Wavelet Transform." In 2013 IEEE 9th International Conference on eScience (eScience). IEEE, 2013. http://dx.doi.org/10.1109/escience.2013.45.

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Dakovic, Milos, Ljubisa Stankovic, Budimir Lutovac, Ervin Sejdic, and Tomislav B. Sekara. "A resistive circuits analysis using graph spectral decomposition." In 2017 6th Mediterranean Conference on Embedded Computing (MECO). IEEE, 2017. http://dx.doi.org/10.1109/meco.2017.7977245.

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Tarun, Anjali, David Abramian, Hamid Behjat, and Dimitri Van De Ville. "Graph Spectral Analysis of Voxel-Wise Brain Graphs from Diffusion-Weighted Mri." In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI). IEEE, 2019. http://dx.doi.org/10.1109/isbi.2019.8759496.

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Feng, Zhuo. "Spectral graph sparsification in nearly-linear time leveraging efficient spectral perturbation analysis." In DAC '16: The 53rd Annual Design Automation Conference 2016. ACM, 2016. http://dx.doi.org/10.1145/2897937.2898094.

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